From 16ddbbec6273dac7923024e9de3ddc0e43d4cb2e Mon Sep 17 00:00:00 2001
From: Robrecht Cannoodt <rcannood@gmail.com>
Date: Sat, 21 Sep 2024 09:37:48 +0200
Subject: [PATCH] remove template metric

---
 src/metrics/accuracy/config.vsh.yaml | 70 ----------------------------
 src/metrics/accuracy/script.py       | 47 -------------------
 2 files changed, 117 deletions(-)
 delete mode 100644 src/metrics/accuracy/config.vsh.yaml
 delete mode 100644 src/metrics/accuracy/script.py

diff --git a/src/metrics/accuracy/config.vsh.yaml b/src/metrics/accuracy/config.vsh.yaml
deleted file mode 100644
index 66fa8359..00000000
--- a/src/metrics/accuracy/config.vsh.yaml
+++ /dev/null
@@ -1,70 +0,0 @@
-# The API specifies which type of component this is.
-# It contains specifications for:
-#   - The input/output files
-#   - Common parameters
-#   - A unit test
-__merge__: ../../api/comp_metric.yaml
-
-
-# A unique identifier for your component (required).
-# Can contain only lowercase letters or underscores.
-name: accuracy
-
-# Metadata for your component
-info:
-  metrics:
-    # A unique identifier for your metric (required).
-    # Can contain only lowercase letters or underscores.
-    - name: accuracy
-      # A relatively short label, used when rendering visualisarions (required)
-      label: Accuracy
-      # A one sentence summary of how this metric works (required). Used when 
-      # rendering summary tables.
-      summary: "The percentage of correctly predicted labels."
-      # A multi-line description of how this component works (required). Used
-      # when rendering reference documentation.
-      description: |
-        The percentage of correctly predicted labels.
-      # A reference key from the bibtex library at src/common/library.bib (required).
-      references:
-        doi: 10.48550/arXiv.2008.05756
-      # The minimum possible value for this metric (required)
-      min: 0
-      # The maximum possible value for this metric (required)
-      max: 1
-      # Whether a higher value represents a 'better' solution (required)
-      maximize: true
-
-# Component-specific parameters (optional)
-# arguments:
-#   - name: "--n_neighbors"
-#     type: "integer"
-#     default: 5
-#     description: Number of neighbors to use.
-
-# Resources required to run the component
-resources:
-  # The script of your component (required)
-  - type: python_script
-    path: script.py
-  # Additional resources your script needs (optional)
-  # - type: file
-  #   path: weights.pt
-
-engines:
-  # Specifications for the Docker image for this component.
-  - type: docker
-    image: openproblems/base_python:1.0.0
-    # Add custom dependencies here (optional). For more information, see
-    # https://viash.io/reference/config/engines/docker/#setup .
-    setup:
-      - type: python
-        packages: scikit-learn
-
-runners:
-  # This platform allows running the component natively
-  - type: executable
-  # Allows turning the component into a Nextflow module / pipeline.
-  - type: nextflow
-    directives:
-      label: [midtime, midmem, midcpu]
diff --git a/src/metrics/accuracy/script.py b/src/metrics/accuracy/script.py
deleted file mode 100644
index 72dcb1e5..00000000
--- a/src/metrics/accuracy/script.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import anndata as ad
-import numpy as np
-import sklearn.preprocessing
-
-## VIASH START
-# Note: this section is auto-generated by viash at runtime. To edit it, make changes
-# in config.vsh.yaml and then run `viash config inject config.vsh.yaml`.
-par = {
-  'input_solution': 'resources_test/task_template/pancreas/solution.h5ad',
-  'input_prediction': 'resources_test/task_template/pancreas/prediction.h5ad',
-  'output': 'output.h5ad'
-}
-meta = {
-  'name': 'accuracy'
-}
-## VIASH END
-
-print('Reading input files', flush=True)
-input_solution = ad.read_h5ad(par['input_solution'])
-input_prediction = ad.read_h5ad(par['input_prediction'])
-
-assert (input_prediction.obs_names == input_solution.obs_names).all(), "obs_names not the same in prediction and solution inputs"
-
-print("Encode labels", flush=True)
-cats = list(input_solution.obs["label"].dtype.categories) + list(input_prediction.obs["label_pred"].dtype.categories)
-encoder = sklearn.preprocessing.LabelEncoder().fit(cats)
-input_solution.obs["label"] = encoder.transform(input_solution.obs["label"])
-input_prediction.obs["label_pred"] = encoder.transform(input_prediction.obs["label_pred"])
-
-
-print('Compute metrics', flush=True)
-# metric_ids and metric_values can have length > 1
-# but should be of equal length
-uns_metric_ids = [ 'accuracy' ]
-uns_metric_values = np.mean(input_solution.obs["label"] == input_prediction.obs["label_pred"])
-
-print("Write output AnnData to file", flush=True)
-output = ad.AnnData(
-  uns={
-    'dataset_id': input_prediction.uns['dataset_id'],
-    'normalization_id': input_prediction.uns['normalization_id'],
-    'method_id': input_prediction.uns['method_id'],
-    'metric_ids': uns_metric_ids,
-    'metric_values': uns_metric_values
-  }
-)
-output.write_h5ad(par['output'], compression='gzip')